Measuring word vector similarity

In this lesson we will understand the power of word vectors using real world trained word vectors. These are word vectors extracted from a list of word vectors published by the Stanford NLP group. A word vector is a sequence or a vector of numerical values. For example, dog = (0.31, 0.92, 0.13)

The distance between word vectors can be measured using a pair-wise similarity metric. Here we will be using sklearn.metrics.pairwise.cosine_similarity. Cosine similarity produces a higher values when the element-wise similarity of two vectors is high and vice-versa.

This exercise is part of the course

Machine Translation with Keras

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Exercise instructions

Print the length of the cat_vector using ndarray.size attribute.
Compute and print the similarity between the cat_vector and window_vector using cosine_similarity.
Compute and print the similarity between the cat_vector and dog_vector using cosine_similarity.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

from sklearn.metrics.pairwise import cosine_similarity

# Print the length of the cat_vector
print('Length of the cat_vector: ', ____.____)

# Compute and print the similarity between cat and window vectors
dist_cat_window = ____(____, window_vector)
print('Similarity(cat, window): ', ____)

# Compute and print the similarity between cat and dog vectors
print('Similarity(cat,dog): ', ____(____, ____))

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